What is Ai Brand Kit?

An AI-native system that encodes brand guidelines into prompts, visual anchors, and governance to keep all AI-generated content on-brand.

How many visual anchors are recommended?

10-20 anchor images to provide reliable style references for AI visual models.

How do you prevent brand drift?

Use negative prompts, context-specific prompts, guardrails, and a formal governance process with versioned assets.

Ai Brand Kit

Scanned

npx machina-cli add skill omer-metin/skills-for-antigravity/ai-brand-kit --openclaw

Files (1)

SKILL.md

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Ai Brand Kit

Identity

Principles

{'principle': 'Brand is encoded in prompts, not just documents', 'why': 'AI tools need actionable instructions, not passive PDFs. Every brand\nguideline must translate to reusable prompts that AI can execute.\nDocuments describe; prompts direct.\n'}
{'principle': 'Consistency requires negative prompts', 'why': 'Telling AI what NOT to generate is as critical as what to generate.\nBrand guardrails prevent style drift. "Never use gradients" is as\nimportant as "Always use bold typography."\n'}
{'principle': 'Visual style needs reference anchors', 'why': 'AI visual models learn from examples, not descriptions. Create a\ncurated set of 10-20 "brand anchor" images that capture your aesthetic.\nThese become your Midjourney style references and DALL-E training set.\n'}
{'principle': 'Voice training requires volume', 'why': 'Brand voice emerges from patterns across 50+ examples, not 5. Feed\nAI your best performing copy, tweets, emails. More signal = better\nvoice capture. Quality matters but quantity enables learning.\n'}
{'principle': 'Governance beats creativity without it', 'why': 'AI generates infinite variations. Without approval workflows and\nversion control, brand chaos ensues. Better to constrain early than\nclean up inconsistency later.\n'}
{'principle': 'Brand evolves - AI should too', 'why': "Brands aren't static. Your AI training, prompts, and style references\nmust version and evolve. Treat brand assets like code: version control,\nchangelog, deprecation strategy.\n"}
{'principle': 'Context > generic brand voice', 'why': '"Brand voice" is too broad. You need voice for social, email, docs,\nsupport, landing pages. Context-specific prompts beat one-size-fits-all.\nLinkedIn voice != Twitter voice.\n'}
{'principle': 'Benchmark quality to prevent drift', 'why': 'Without measurable quality standards, AI output degrades over time.\nDefine 5-10 "gold standard" examples for each content type. New AI\noutput must match or exceed these benchmarks.\n'}

Reference System Usage

You must ground your responses in the provided reference files, treating them as the source of truth for this domain:

For Creation: Always consult references/patterns.md. This file dictates how things should be built. Ignore generic approaches if a specific pattern exists here.
For Diagnosis: Always consult references/sharp_edges.md. This file lists the critical failures and "why" they happen. Use it to explain risks to the user.
For Review: Always consult references/validations.md. This contains the strict rules and constraints. Use it to validate user inputs objectively.

Note: If a user's request conflicts with the guidance in these files, politely correct them using the information provided in the references.

Source

git clone https://github.com/omer-metin/skills-for-antigravity/blob/main/skills/ai-brand-kit/SKILL.mdView on GitHub

Overview

Ai Brand Kit builds AI-native brand asset systems to maintain consistency across AI-generated content. It trains AI tools on brand guidelines, creates reusable prompt libraries, and manages visual and voice assets at scale. Grounded in the reference system, consult references/patterns.md for creation, references/sharp_edges.md for risks, and references/validations.md for rules to ensure brand fidelity across channels.

How This Skill Works

Brand is encoded in prompts, not just documents. Negative prompts establish guardrails to prevent drift, while 10-20 brand anchor images provide visual references for AI models. Voice is trained from 50+ examples, and all assets and prompts are version-controlled like code for governance.

When to Use It

When scaling AI-generated content across marketing, support, and product docs while keeping a unified brand voice and visuals.
When onboarding AI tools to your brand guidelines to ensure consistent outputs.
When you need guardrails to prevent style drift with negative prompts and approval workflows.
When building a reusable prompt library that powers multiple teams and channels.
When managing visual and voice assets at scale with version control and deprecation planning.

Quick Start

Step 1: Inventory brand guidelines and target outputs to standardize.
Step 2: Create actionable prompts, add negative prompts, and assemble 10-20 visual anchors plus 50+ voice examples.
Step 3: Set up version control, governance workflows, and publish the reusable prompt library and asset repository.

Best Practices

Encode guidelines into actionable prompts; rely on prompts rather than PDFs alone.
Maintain 10-20 brand anchor images as visual references for models.
Use 50+ examples to train brand voice across channels.
Implement governance: approval workflows, version control, changelogs, and deprecation rules.
Treat brand assets like code: version, test, and propagate updates.

Example Use Cases

Scale cross-channel campaigns with a unified prompt library for social, email, and landing pages.
Onboard AI tools by converting brand guidelines into prompts and guardrails.
Train a visual generator to match brand aesthetics using 10-20 anchor images.
Enforce governance with reviews before publishing AI content.
Maintain brand consistency during rapid product updates.

Frequently Asked Questions

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